6 WAYS TO PUT PEOPLE AT THE HEART OF YOUR DATA TRANSFORMATION

POWER TO YOUR

PEOPLE

THE BEATING HEART OF CHANGEData transformation.

Let’s wipe the slate clean. What is it?

Data transformation is the process of using your data to unlock actionable insights that can transform your business and deliver sustained competitive advantage.

But first, you need the ability to use it.

Because applied correctly, data is enabling organisations in all sectors from retail, healthcare and pharmaceuticals to transport, banking and insurance to do things like:

Sharpen customer insights and personalisation to boost relevance and loyalty Develop more sophisticated

pricing models and product ranges to grow revenue Tackle fraud, risk and

compliance issues

Drive sustainable efficiency improvements across business operations Level-up your talent lifecycle – from

recruitment and development to engagement and retention

Ultimately, transformation is your pathway to adopting advanced technologies like AI and machine learning, helping you drive new innovations and build a sustainable competitive edge.

Still, success is no guarantee. 85% of data-led transformation projects fail to deliver their promised benefits.1

How do you ensure your projects end up in the other 15%?

Hint: Data transformation isn’t all about data. Or navigating the endless landscape of technology options. It’s about people – your people.

1 https://www.techrepublic.com/article/85-of-big-data-projects-fail-but-your-developers-can-help-yours-succeed/

HERE ARE 6 WAYS TO MAKE IT HAPPEN:

SO, HOW DO YOU PUT YOUR PEOPLE AT THE CENTRE OF YOUR DATA TRANSFORMATION?

1 GET YOUR DATA AND BUSINESS STRATEGIES ON SPEAKING TERMS

of organisations report having an enterprise-wide data strategy.2

Just

23%

SOUND FAMILIAR?“We don’t know what data we have or what value we can get from it.”

“Our leaders don’t get how data can help to deliver strategic objectives.”

“We’re working on a range of data initiatives. But I’m not quite sure what they are... or what we expect to get from them.”

TRANSFORMATION TIP #1 Valuable data lives in every aspect of a business – from finance and IT to marketing and HR – so no business plan is complete without a data strategy. Building one requires new levels of collaboration between every business team, because data transformation will also fail if a strategy is only adopted in silos.

Start by really understanding your company’s strategy. What are the major objectives to grow revenue or market share? Or to innovate, better serve customers and drive efficiencies? Your data strategy needs to address how it will deliver on these wider objectives, or risk irrelevance.

2 https://www.gkapprenticeships.com/employers/technology-provider-apprenticeships/ibm-apprenticeships

Too often, an organisation’s data strategy is disconnected from its business strategy. That puts people in the crosshairs, trying to make sense of it all while driving the business forward – and inevitably failing.

2 MAKE SURE YOUR DATA OWNERS GIVE A MONKEY’S ABOUT DATA

of IT leaders admit: they don’t always know who owns data within their organisation.3

57%

SOUND FAMILIAR?“Honestly, I couldn’t tell you who owns our data at the executive level.”

“Data accountability? Between the CDO, CIO and CEO, it’s like Game of Thrones.”

“The CDO was our project sponsor. Then the CEO took an interest. Wish us luck.”

Many business leaders seem to believe that their data will look after itself. But without clear data leadership, even the brightest data teams will struggle to deliver meaningful innovation and affect change.

3 http://bigdata-madesimple.com/exciting-facts-and-findings-about-big-data/

TRANSFORMATION TIP #2 Data transformation only happens with the right human leadership. But many execs are still finding their place in the process. While the role of CDO is beginning to come into its own, data leadership must be a team game across the C-suite, where each person has a clearly defined set of responsibilities.

Map out your team structure and responsibilities, then run workshops to demonstrate how it should work – end-to-end and in practice, with real life data initiatives and examples.

Finally, be clear on the face of your transformation. Who ultimately owns your data at board level? This person should be visible, dynamic, data-savvy, and able to break through the inevitable barriers.

3 SUPPORT YOUR NEW DIRECTION WITH THE RIGHT TALENT AND STRUCTURE

In 2018, global demand for Data Scientists exceeded supply by more than

50%4

SOUND FAMILIAR?“Achieve data transformation without changing our people or structure? This should go well.”

“Where do we even find the right data talent?”

“Hire new data talent? We have enough trouble keeping hold of the staff we do have.”

It’s easy to get excited about a grand vision for the future. But roadmaps often focus too heavily on the destination at the expense of the very thing that makes change possible: people, positioned for success.

4 https://insidebigdata.com/2018/08/19/infographic-data-scientist-shortage/

TRANSFORMATION TIP #3 Your people can’t be expected to deliver data transformation from a standing start, armed with their current capabilities. And given the global shortage of specialist data talent, you need to think creatively – and long-term.

Your teams need the right blend of deep analytics and data science skills. They also need time to develop an understanding of the systems and tools they use to support

your business. Along with that, they need a curious mindset and an appreciation for cross-business collaboration.

To find the right people, you can: 1) hire effectively and teach data capabilities through training, on-the-job experience and sharing of best practice, or 2) outsource to experienced data analytics providers. Both are valid options, and both will likely play a role in your transformation. The trick is to understand the optimum mix at any given time, and keep one eye on building

long-term sustainability.

So take the time to consider data skills as a core part of your upfront planning, informing and shaping your company’s data operating model. And because there’s no single blueprint, your people should help shape the model that’s right for you. Remember: it’s better to prove value and grow incrementally than flood your organisation with new faces, then find you have a credibility problem if they fail to deliver.

4 DITCH THE DEAD-END DATA INITIATIVES AND PET PROJECTS

of data scientists say their biggest challenge is a lack of management support.

say they don’t know the question they’re being asked to answer.5

SOUND FAMILIAR?“We either collect no data or collect everything… and do nothing with it. It’s analysis paralysis.”

“Our data teams answer to themselves – working on whatever they like.”

“The data team is cut off from business leaders and doesn’t have a clear picture of what’s expected.”

We’re all for data experimentation and freedom to explore. But without a pragmatic framework for prioritising and measuring data initiatives, innovation can rapidly become rudderless and invisible.

5 https://www.kaggle.com/surveys/2017

40%Nearly

30%Another

TRANSFORMATION TIP #4 Seems simple: align your data projects to where they can make the biggest impact. But never run a project in isolation. Offer a clear set of short- and long-term KPIs, tied to larger data and business strategies.

Beyond the micro, establish a lean portfolio-prioritisation mindset across your data initiatives, with objective criteria that can challenge results. You should be asking questions like: What are the benefits of the opportunity? Is the data accessible – and what’s the cost of acquisition? Is this a business priority?

Will a cheap ‘n’ cheerful pilot prove value? Can we scale if the results are positive?

Done well, this will focus innovation and eliminate pet projects, ensuring your expensive data teams pull in the right direction.

5 MAKE YOUR DATA INSIGHTS A MEANS TO AN END, NOT AN END IN ITSELF

of organisations want to be “data-driven”, but only 29% succeed in connecting action and analytics.6

74%

SOUND FAMILIAR?“I’ve produced a load of data insights. But they never lead to action.”

“We’re caught in the ‘Pilot Trap’ – experimenting, but not scaling.”

“How do we measure a project’s success – or know what it means for our transformation?”

TRANSFORMATION TIP #5 Gaining a tangible business benefit is the only reason to undertake any data transformation project. Without a clear benefit, the whole effort becomes pointless.

Agree upfront which KPIs, benefits and outcomes you are seeking. Measure them all relentlessly before, during and after they’ve run their course. Then make these metrics a core part of your data teams’ objectives and incentivisation, tying their success and efforts directly to the bigger picture.

Initially, your teams may be uncomfortable with this level of transparency and scrutiny. Hang in there during the early frustrations. Over time, you’ll see a greater accountability for outcomes (not just insights), and your teams will appreciate being able to see and measure the value they bring to the organisation.

Data teams unlock potentially impactful data insights on a regular basis. And far too often, “potential” is as far as it goes – with organisations failing to turn insights into actions that deliver meaningful change and results.

6 http://blogs.forrester.com/brian_hopkins/16-03-09-think_you_want_to_be_data_driven_insight_is_the_new_data

6 BRING YOUR PEOPLE ALONG FOR THE RIDE

of the challenges organisations face when adopting big data relate to culture and people – while just 5% relate to technology.7

95%

SOUND FAMILIAR?“Where do I fit into our transformation?”

“Is my job secure?”

“Will I even like what my job becomes?”

“I’m more than data points on some dashboard.”

TRANSFORMATION TIP #6 Data transformation is a deeply human endeavour. (Rarely does it involve a fully-automated solution without a human to be seen.) Behind every initiative is a set of hopes, aspirations and benefits as well as a sense of fear, uncertainty and dread about what the new world might bring.

But you can’t simply ignore these human concerns. You need to help your people see transformation for what it can and should always be: a mutually-beneficial partnership with data.

To do that, put a human face on your data transformation. Build and communicate a compelling data story, openly addressing

employee concerns, dispelling rumour and myths, and articulating why transformation is important – what’s in it for your people and how they’ll be supported.

Do this regularly and sincerely throughout your transformation, and you’ll stand a much greater chance of achieving your goals.

When business leaders talk about things like AI and machine learning, the future can seem limitless. But when leaders fail to align stakeholders or address legitimate staff concerns, transformation jumps the tracks.

7 https://www.forbes.com/sites/ciocentral/2019/01/02/what-we-learned-from-top-execs-about-their-big-data-and-ai-initiatives/#21ed3afb452a

POWERING YOUR TRANSFORMATION ON PEOPLE POWERIf successful data transformation is about creating the culture, structure and governance your people need to feel bought-in and supported, where do you start?

First, take a holistic, people-first approach. Then identify and prioritise the projects that have the best chance of success and can deliver value the fastest. Most importantly, don’t get lost in the data and technology. Stay focussed on what you want your business to become – and never lose sight of how your people can help you get there.

But your people can’t do it alone either. Give them a data transformation partner that can help:

Identify the right data strategy, benefits and data projects

Design and enable the right data organisation and people

Deliver complex data projects and “Last Mile” business benefits

Do all that, and you can unlock a world of machine learning and AI-driven opportunity.

(At the very least, you won’t be in the 85%.)

WANT TO KNOW MORE?Get in touch with Gate One to learn more about our data services and how we can help you to make a success of your data transformation.

Emaildavid.holliday@gateone.co.uk

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